Cross-domain Nuclei Detection in Histopathology Images using Graph-based Nuclei Feature Alignment

Area of Science:

• Computational pathology
• Medical image analysis
• Deep learning

Background:

• Deep neural networks (DNNs) excel at nuclei detection in histopathology images but are sensitive to domain shift.
• Domain shift, common in real-world data, significantly degrades DNN performance.
• Existing domain adaptation methods face challenges with small nuclei size and noisy features.

Purpose of the Study:

• To develop an effective method for nuclei detection across different histopathology image domains.
• To address the limitations of insufficient nuclei features and background noise in domain adaptation.
• To enhance the robustness and accuracy of nuclei detection despite domain variations.

Main Methods:

• Propose a graph-based nuclei feature alignment (GNFA) method.
• Utilize a nuclei graph convolutional network (NGCN) to aggregate adjacent nuclei information for feature generation.
• Incorporate an importance learning module (ILM) to select discriminative features and mitigate background noise.

Main Results:

• The proposed GNFA method effectively aligns features across domains.
• It successfully mitigates the negative impact of domain shift on nuclei detection.
• Achieves state-of-the-art performance in cross-domain nuclei detection scenarios.

Conclusions:

• The GNFA method provides a robust solution for cross-domain nuclei detection.
• It overcomes key challenges related to feature representation and alignment in domain adaptation.
• Demonstrates significant improvements over existing methods in extensive experiments.